Exemplo n.º 1
0
def main(_):
    if not os.path.exists(FLAGS.sample_dir):
        os.makedirs(FLAGS.sample_dir)

    # gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5)
    # run_config = tf.ConfigProto(gpu_options=gpu_options)
    run_config = tf.ConfigProto()
    run_config.gpu_options.allow_growth = True

    if FLAGS.ae:
        with tf.Session(config=run_config) as sess:
            bsp_ae = BSP_AE(
                sess,
                FLAGS.phase,
                FLAGS.sample_vox_size,
                is_training=FLAGS.train or FLAGS.getz,
                dataset_name=FLAGS.dataset,
                checkpoint_dir=FLAGS.checkpoint_dir,
                sample_dir=FLAGS.sample_dir,
                data_dir=FLAGS.data_dir)

            if FLAGS.train:
                bsp_ae.train(FLAGS)
            elif FLAGS.getz:
                bsp_ae.get_z(FLAGS)
            else:
                if FLAGS.phase == 0:
                    bsp_ae.test_dae3(FLAGS)
                else:
                    # bsp_ae.test_bsp(FLAGS)
                    bsp_ae.test_mesh_point(FLAGS)
                    # bsp_ae.test_mesh_obj_material(FLAGS)
    elif FLAGS.svr:
        with tf.Session(config=run_config) as sess:
            bsp_svr = BSP_SVR(
                sess,
                FLAGS.phase,
                FLAGS.sample_vox_size,
                is_training=FLAGS.train,
                dataset_name=FLAGS.dataset,
                checkpoint_dir=FLAGS.checkpoint_dir,
                sample_dir=FLAGS.sample_dir,
                data_dir=FLAGS.data_dir)

            if FLAGS.train:
                bsp_svr.train(FLAGS)
            else:
                # bsp_svr.test_bsp(FLAGS)
                bsp_svr.test_mesh_point(FLAGS)
                # bsp_svr.test_mesh_obj_material(FLAGS)
    else:
        print("Please specify an operation: ae or svr?")
Exemplo n.º 2
0
def main(_):
	if not os.path.exists(FLAGS.sample_dir):
		os.makedirs(FLAGS.sample_dir)

	#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5)
	#run_config = tf.ConfigProto(gpu_options=gpu_options)
	run_config = tf.ConfigProto()
	run_config.gpu_options.allow_growth=True

	with tf.Session(config=run_config) as sess:
		bsp_ae = BSP_AE(
				sess,
				FLAGS.sample_vox_size,
				is_training = FLAGS.train,
				dataset_name=FLAGS.dataset,
				checkpoint_dir=FLAGS.checkpoint_dir,
				sample_dir=FLAGS.sample_dir,
				data_dir=FLAGS.data_dir)

		if FLAGS.train:
			bsp_ae.train(FLAGS)
		else:
			bsp_ae.test(FLAGS)
Exemplo n.º 3
0
parser.add_argument("--sample_vox_size", action="store", dest="sample_vox_size", default=64, type=int, help="Voxel resolution for coarse-to-fine training [64]")
parser.add_argument("--train", action="store_true", dest="train", default=False, help="True for training, False for testing [False]")
parser.add_argument("--start", action="store", dest="start", default=0, type=int, help="In testing, output shapes [start:end]")
parser.add_argument("--end", action="store", dest="end", default=16, type=int, help="In testing, output shapes [start:end]")
parser.add_argument("--ae", action="store_true", dest="ae", default=False, help="True for ae [False]")
parser.add_argument("--svr", action="store_true", dest="svr", default=False, help="True for svr [False]")
parser.add_argument("--getz", action="store_true", dest="getz", default=False, help="True for getting latent codes [False]")
FLAGS = parser.parse_args()



if not os.path.exists(FLAGS.sample_dir):
	os.makedirs(FLAGS.sample_dir)

if FLAGS.ae:
	bsp_ae = BSP_AE(FLAGS)
	print("starting in AE")
	if FLAGS.train:
		bsp_ae.train(FLAGS)
	elif FLAGS.getz:
		bsp_ae.get_z(FLAGS)
	else:
		if FLAGS.phase==0:
			bsp_ae.test_dae3(FLAGS)
			# bsp_ae.test_bsp(FLAGS)			
		else:
			# bsp_ae.test_dae3(FLAGS)
			bsp_ae.test_bsp(FLAGS)
			bsp_ae.test_mesh_point(FLAGS)
			# bsp_ae.test_mesh_obj_material(FLAGS)
elif FLAGS.svr: